There are clouds in the sky! Let the points rain down onto the Earth’s surface and turn them into visual and analytical elevation-related products.
LiDAR point clouds are becoming more popular, but why? And given how big the files are, how do you quickly make use of them in ArcGIS?
What is LiDAR?
LiDAR is an acronym for Light Detection and Ranging, which simply means that there are light pulses being shot from an airborne or ground-based sensor that reflect off a surface. The time it takes for the reflected pulse to return to the sensor is used to determine how far away the object is. This post will discuss airborne LiDAR, but the technology is also commonly used with autonomous vehicles to determine how far away the car is from obstacles.
Post-processing of the light pulses results in several standardized files, called LAS files, being generated that store information for each of the millions of points. Details such as elevation, brightness of the object that reflected the pulse (intensity) and even what the pulse reflected off of (a classification code) are captured.
Why is LiDAR becoming more popular?
Millions of points means lots of detailed information so it is possible to create realistic urban landscapes, also known as digital twins, and to do high-resolution analysis on vegetation such as determining the amount of harvestable wood from a forest of trees.
Changes in elevation between 2013 and 2018 derived from LiDAR. White shows increase in height and black shows decrease in height. Three new buildings were constructed, two new ones are under construction and trees have grown while other trees have fallen or been removed.
What has changed more recently is that the computer power required to process the data has become cheaper and faster. But how does one start using the LiDAR data?
The LAS dataset
Initially, I start by creating an LAS Dataset in ArcGIS Pro to quickly visualize the point cloud. That allows me to do quality checks and modify the classification codes, if necessary, to remove noise or other problematic points, like the construction cranes in the image above. The classification codes are really helpful for extracting subsets of points for feature identification, like buildings and trees.
The mosaic dataset
Next, I convert the points into a raster surface using a mosaic dataset. By choosing which points to use in the surface, I can create a digital elevation model (DEM) that captures ground heights and a digital surface model (DSM) that captures heights of structures. Later, I will use raster functions to generate a mosaic dataset that shows the heights of all the building structures, trees and other objects on the surface. In the image above, I repeated the process for two different years and then compared the elevations at each pixel.
To learn more about how to work with LiDAR data in ArcGIS: